Methods, apparatus and systems for data sorting, visualisation and related applications

ABSTRACT

A method of creating a style sheet for the generation of a graphical representation of a data set made up of a plurality of data elements or values used in a data visualisation computing system is described. The method having the steps of retrieving the data values of the data elements in the data set; determining the types of data elements in the set; obtaining the style attributes associated with each type of data element; developing a hierarchy of style attributes; and creating a style sheet to apply to the data set.

This application claims benefit of U.S. Provisional Ser. No. 61/862,702,filed 6 Aug. 2014 and which application is incorporated herein byreference. To the extent appropriate, a claim of priority is made to theabove disclosed application.

FIELD OF THE INVENTION

The present invention relates to methods, apparatus and systems for datasorting, visualisation and related applications.

BACKGROUND

The use of style sheets for coding is a way that presentation structurecan be separated from the detail of the content. For example, in webdesign, the markup language (i.e. HTML or XHTML) of a webpage containsthe semantic content and structure for the page, but does not define itsvisual layout or style. The style is defined in an external file—thestyle sheet file—using a style sheet language such as CSS or XSLT.Previous to this design approach, the methodology was to have both styleand structure defined by the markup language of a page. In short, astyle sheet is a file that contains a set of rules which are used toimpose formatting information on a data set or sets which are held as aseparate file or files.

For example, in word processing and desktop publishing, a style sheet isa file or form that defines the layout of a document. The style sheetspecifies parameters such as the page size, margins, and fonts. Theseparameters can then be carried over to a number of files or documents togive a uniform appearance for documents that contain different detailedcontent. Different style sheets can also be defined, for example, onefor official reports, one for letters, one for team meeting memos orminutes, one for action plans, etc.

Prior to the advent and use of style sheet languages such as CSS, thecommonest way to format a file was to include or contain thepresentational attributes within the HTML markup; all font colours,background styles, element alignments, borders and sizes were codedwithin the HTML and as a consequence it was often required to repeat theinformation within the HTML. The advent of CSS allowed much of thisformatting information to be moved to the style sheet file. This allowedthe HTML language to be considerably simplified. Also, holding all thepresentation styles in one file helped to significantly reducemaintenance time, and helps to reduce human error. For example, the fontcolour associated with a type of text element may be specified—andtherefore easily modified—throughout an entire website simply bychanging the line or lines of code in the style sheet file. The previousor alternate approach, using styles embedded in each individual page,required the same edit to be carried out for every page. As this was acumbersome and time consuming process, errors tended to creep in due tohuman error.

An “external” style sheet file can be associated with an HTML documentor documents to format the presentation of the document(s). Multiplestyle sheets can also be ‘called’ or imported. If multiple style sheetsare used, these can be prioritised, with the style sheet with thehighest priority controlling the content display. Declarations not setin the highest priority source are passed on to a source of lowerpriority, such as the user agent style. This process is calledcascading.

A cascading style sheet is a style sheet that anticipates that otherstyle sheets will either fill in or override the overall style sheet.This provides the designer the advantage of being able to rely on thebasic style sheet when desired and overriding it when desired. Thefilling in or overriding can occur on a succession of “cascading” levelsof style sheets. For example, one style sheet could be created andlinked to from every separate page of a website as the overall stylesheet (e.g. to set font type, font size, text colour, background, etc).For any portion of a page that includes a certain kind of content—forexample, a catalogue of products—another style sheet can be used, thissecondary style sheet amending the basic style sheet for that particularcontent. Another style sheet could be specified as applying to aparticular type of product display.

As well as style sheets which control visual or display elements such astext size and colour, the style sheets may also include functionalaspects. For example, the style sheet can include a function to sortdata included in a file, as specified by the style sheet. It is alsoknown to combine data tables with table specific style sheets whichperform functions such as sorting, to create a table or similar whichhas an overall layout and look dictated by a style sheet, and where thedata is sorted as dictated by a style sheet.

Although a separate style sheet or sheets can be created and applied toa page or data set as required to format the presentation or order of adocument or documents, one issue with the use of style sheets is thatfor each particular required situation, an individual style sheet isstill necessary. For multiple separate sets of data, it is stillnecessary to apply and maintain separate style sheets. If a number ofdifferent formats are potentially required, these will need to bethought out beforehand, and a number of style sheets will have to becreated and prioritised/cascaded as required for each different format.This potentially requires the allocation of increased memory, andincreased processing power to achieve the end result.

It is an object of the present invention to provide a method of sortingand visualising data which goes some way to overcoming theabove-mentioned disadvantages or which at least provides the public orindustry with a useful choice.

It is a further object of the present invention to provide an apparatusfor sorting and visualising data which goes some way to overcoming theabove-mentioned disadvantages or which at least provides the public orindustry with a useful choice.

Further objects and advantages of the invention will be brought out inthe following portions of the specification, wherein the detaileddescription is for the purpose of fully disclosing the preferredembodiment of the invention without placing limitations thereon.

The background discussion (including any potential prior art) is not tobe taken as an admission of the common general knowledge.

SUMMARY OF THE INVENTION

The term “comprising” as used in this specification and indicativeindependent claims means “consisting at least in part of”. Wheninterpreting each statement in this specification and indicativeindependent claims that includes the term “comprising”, features otherthan that or those prefaced by the term may also be present. Relatedterms such as “comprise” and “comprises” are to be interpreted in thesame manner.

As used herein the term “and/or” means “and” or “or”, or both.

As used herein “(s)” following a noun means the plural and/or singularforms of the noun.

In a first aspect, the present invention provides a data visualisationcomputing system, a computer implemented method of creating a stylesheet for the generation of a graphical representation of a data setmade up of a plurality of data elements or values, the method having thesteps of:

-   -   retrieving the data values of the data elements in the data set;    -   determining the types of data elements in the set;    -   obtaining the style attributes associated with each type of data        element;    -   developing a hierarchy of style attributes; and    -   creating a style sheet to apply to the data set.

BRIEF DESCRIPTION OF THE DRAWINGS

Further aspects of the invention will become apparent from the followingdescription which is given by way of example only and with reference tothe accompanying drawings which show embodiments of the device by way ofexample, and in which:

FIG. 1 shows a schematic overview of the architecture of a systemsuitable for the preferred embodiments, with the main building blocks orsystem elements and the main connections between these elements shown;

FIG. 2 shows a schematic overview flow diagram of the method as used forthe preferred embodiments; and

FIG. 3 shows a schematic view of elements within a gaming environment,such as game machines and gaming tables, and associated elements such asATMs, with communication connections between these and a local datastore and a main offsite data store also shown.

DETAILED DESCRIPTION OF THE INVENTION

According to various embodiments of the present invention, systems andmethods are herein described in which the types of data elements in adataset or datasets are first assessed or determined, and a set of styleattributes or rules is then associated with each type of element. Ahierarchy of style attributes is then developed for the data elements,the hierarchy used to create a style sheet which is then applied to thedata set.

Although the various specific embodiments described below are inrelation to the representation of data in table form, it will beunderstood that the methods and systems described may also be applied toother suited graphical representation methods, such as data plots andgraphs for example.

System Hardware and Architecture

The methods and systems described may be utilised on any suitable datavisualisation system where data values are represented in table cells,or as data points which are displayed using various different formatsbased on the data value itself or a range of values in which the datavalue lies (e.g. bar chart, pie chart, scatter plot, line chart, heatmap, box spiral, etc).

According to the embodiments described below, a data sorting andvisualisation computing system utilises the methodology of the inventionusing various modules and engines.

The data sorting and visualisation system includes at least a processor,one or more memory devices or an interface for connection to one or morememory devices, input and output interfaces for connection to externaldevices in order to enable the system to receive and operate uponinstructions from one or more users or external systems, a data bus forinternal and external communications between the various components, anda suitable power supply. Further, the data sorting and visualisationsystem may include one or more communication devices (wired or wireless)for communicating with external and internal devices, and one or moreinput/output devices, such as a display, pointing device, keyboard orprinting device.

The processor is arranged to perform the steps of a program stored asprogram instructions within the memory device. The program instructionsenable the various methods of performing the invention as describedherein to be performed. The program instructions, may be developed orimplemented using any suitable software programming language andtoolkit, such as, for example, a C-based language and compiler. Further,the program instructions may be stored in any suitable manner such thatthey can be transferred to the memory device or read by the processor,such as, for example, being stored on a computer readable medium. Thecomputer readable medium may be any suitable medium for tangibly storingthe program instructions, such as, for example, solid state memory,magnetic tape, a compact disc (CD-ROM or CD-R/W), memory card, flashmemory, optical disc, magnetic disc or any other suitable computerreadable medium.

The data sorting and visualisation system is arranged to be incommunication with data storage systems or devices (for example,external data storage systems or devices) in order to retrieve therelevant data.

It will be understood that the system herein described includes one ormore elements that are arranged to perform the various functions andmethods as described herein. The embodiments herein described are aimedat providing the reader with examples of how various modules and/orengines that make up the elements of the system may be interconnected toenable the functions to be implemented. Further, the embodiments of thedescription explain, in system related detail, how the steps of theherein described method may be performed. The conceptual diagrams areprovided to indicate to the reader how the various data elements areprocessed at different stages by the various different modules and/orengines.

It will be understood that the arrangement and construction of themodules or engines may be adapted accordingly depending on system anduser requirements so that various functions may be performed bydifferent modules or engines to those described herein, and that certainmodules or engines may be combined into single modules or engines.

It will be understood that the modules and/or engines described may beimplemented and provided with instructions using any suitable form oftechnology. For example, the modules or engines may be implemented orcreated using any suitable software code written in any suitablelanguage, where the code is then compiled to produce an executableprogram that may be run on any suitable computing system. Alternatively,or in conjunction with the executable program, the modules or enginesmay be implemented using, any suitable mixture of hardware, firmware andsoftware. For example, portions of the modules may be implemented usingan application specific integrated circuit (ASIC), a system-on-a-chip(SoC), field programmable gate arrays (FPGA) or any other suitableadaptable or programmable processing device.

The methods described herein may be implemented using a general purposecomputing system specifically programmed to perform the described steps.Alternatively, the methods described herein may be implemented using aspecific computer system such as a data sorting and visualisationcomputer, a database query computer, a graphical analysis computer, agaming data analysis computer, a manufacturing data analysis computer, abusiness intelligence computer etc., where the computer has beenspecifically adapted to perform the described steps on specific datacaptured from an environment associated with a particular field.

Overview

FIG. 1 is a schematic diagram showing the architecture of the system ofthe preferred embodiment, with the main building blocks or systemelements and the main connections between these elements shown. A datastore 101 is shown. The data store 101 can be an enterprise datawarehouse, an operational data store, a data mart, a storage array, orsimilar, and can be of the type which receives and stores data frommultiple sources 102, which may be widely geographically separated.Further, the data store may be a cache memory used to temporarily storeincoming data captured in real time—for example streaming data. The datastore may also be a central location, or a distributed network. Asexamples (but not limited to these), the data could relate to the use ofATMs, use of vending machines, cell phone use, gaming machine use,product sales, take-up of a particular service, stock or commodityprices, currency exchange rates, etc. Some or all of the data from thesepoint-of-service machines could be uploaded automatically to the datastore 101 via a network. There may be several different types ofparticular data associated with each individual entry. For example, forATM use each individual withdrawal might make up a data element, and themachine location, the time of use and the amount withdrawn could beassociated with each individual use or data element. For cell phone use,each individual call might make up an individual data element, withlocation, time and call duration associated with the individual dataelement. Similarly, game machine use could record an individualtransaction, such as an individual ‘buy-in’ or use, with the monetaryamount, the time of use, and the machine location all associated withthis individual data element. It can be seen that other data elementscan be iterated from these: frequency of use (e.g. for ATMs or gamingmachines), locational density (e.g. number of cell phone calls made fromor to a particular cell), user usage frequency (every time an individualuser uses a service), etc. The data can be sent to the data store 101from the sources 102 by way of any suitable communication system 103—forexample, wireless transmission, transmission via an establishedtelephone network (mobile or landline), via a built-in hardwired grid,etc. A data retrieval engine 104 is in communication with the data store101 to enable the stored data to be retrieved and transferred to otherelements of the system. A processing module 105 is in communication withthe data retrieval engine to receive the data and process this asoutlined below. The data retrieval engine 104 and processing module 105are in communication with external inputs from a terminal or interface106 which can be, for example, a user interface for inputting commandsor inserting programs or code. A rendering engine 107 is incommunication with the processing module 105. The rendering engine 107is arranged to render the data elements to create a display on an imagespace on an output device 108 as instructed by the processing module105. For example, the rendering engine 107 would receive instructions tocreate a table, and in accordance with the received instructions, wouldcreate the table on the output device 108 and populate the cells of thetable with data elements in the locations specified by the processingmodule 105. A printer element 109 is also shown in parallel with therendering engine 107 and the output device 108. The printer device 109can be used to produce hardcopy of the result of the application of thestyle sheet to the retrieved data elements by the processing module 105.The rendering engine can be adapted to produce outputs suitable for bothon-screen/on-line, and hardcopy or print output. Also shown is atransmission unit 110. The transmission unit 110 can take the output ofthe processing module and transmit this in a suitable format—e.g. as ane-mail to a smartphone, suitably formatted for reading via e-mail.

FIG. 2 shows a flow diagram of the steps of the general method of thepresent invention. The herein described system utilises the computerimplemented method to retrieve or obtain data values, develops ahierarchy of style attributes to apply to the data values, sorts anddetermines the types of data elements in the set; and applies the styleattributes to the data elements to create a style sheet to apply to thedata set.

In step 201, a data set made up of a plurality of data values isretrieved from the data store 101. This can be in response to a userinput command where a user requests certain data, which can be boundedas required (e.g. a date range, sales within certain upper and lowerlimits, etc). In step 202, the types of data elements in the retrievedset are determined or assessed. Particular examples of the types of dataelements which could be retrieved and assessed are outlined below in thepreferred embodiments. Next, in step 203, the style attributesassociated with each type of data element are obtained. Examples ofpossible style attributes are given below for the preferred embodiments.In step 204, a hierarchy of style attributes is developed. The hierarchydictates how the elements are ordered and formatted, and so in step 205,a style sheet is created which is applied to the retrieved data elementsin step 206. In the final step 207, after the style sheet has beenapplied to the data elements, the result is rendered and displayed on anoutput device as outlined above. The line 210 shows how a user canreview the results from the final step, and can change the weighting ofthe style attributes so that the hierarchy of style attributes changes,changing the style sheet which is created in step 205. The line 211shows a user input at the beginning of the process, so that rather thanaccept the automatic weightings which may be applied to the styleattributes of the data elements (a conditional priority approach,described in greater detail below), the user enters these manuallybefore the subsequent steps are carried out (a user-set or user-dictatedpriority approach, which is described in more detail below).

In the process outlined above, the data set is retrieved from the datastore 101. A user enters the required fields via the terminal orinterface 106, so that the data retrieval engine 104 can retrieve thedata elements from the data store 101. These fields could be, forexample: a date range or ranges, number or units of sales, incidents ofuse over a time period, locations, etc. There can also be sub-fieldsassociated with the main fields—for example, the date range could returndata relating to sales over the range of a year or years, with furthersub-detail of sales per month, week or day within that particular range.Incidents of use could be numbered per week, with further sub-detailshowing incidents of use per day (every 24 hours), and sub-sub-detail ofthe timing of incidents of use within that period. This level of detailcan be useful for pattern analysis. For example, when analysing usage,weekday usage may be heavier or lighter than weekend use, and this canbe useful for planning maintenance or developing sales strategies.

There are two main ways in which the hierarchy of style attributes canbe developed once the data elements are retrieved: firstly, aconditional priority approach, and secondly, a user-set or user-dictatedpriority approach.

For a conditional priority approach, the particular combination offields chosen by a user dictates the hierarchy with no further inputrequired. As an example, a user may firstly choose a date range spacedover a number of years, plus sales figures. The processing module 105receives the data and processes this to create the style sheet. As thedate range is over a number of years, this is given the highestpriority. The sales data is given a lower priority than the date range.Depending on the data chosen, the processing module 105 may applydefault settings to certain elements. For example, if a number of yearsare chosen, the default for the style sheet may be to show a ‘year’ viewonly, or the default may be to show both years and months as a sub-rangewithin those years. However, knowing the relative default priority ofthe two main elements (time range, and sales), the style sheet which isdeveloped will place ‘time’ horizontally, in either the column cells ofa table, or along the x-axis of a graph, and sales data vertically, inthe row cells of a table, or along a y-axis. The type and range of datacan also dictate presentation output—for example, if the number of yearschosen is between a certain minimum and maximum number, and only thetotal sales figures are retrieved, then the presentation style maydefault to a histogram, bar chart or similar. If the range chosen isoutside this range, then the display may revert to another style—forexample if a single year is chosen, the display may revert to showingsales by individual month on a bar chart, so that 12 bars are shownrather than a single bar.

If a third parameter such as location is also chosen, this is given thelowest priority, and the style sheet created will default to showingthis data within the bounds of the output dictated by the two elements(time and sales) with higher priority. An example of this is describedfor the first embodiment below.

For a user-set or user-dictated priority approach, the user firstlychooses the data elements which they require, and then manually assignsor forces a priority onto each of these, to create the desiredhierarchy. The user can either accept the default output displayoptions, or manually choose an output style. Once a first displayiteration is completed, the user can accept this as the end result if itis to their satisfaction, or they can refine the output, by changing theoutput style (e.g. from a bar chart to a line graph or similar), or bychanging the priority to change the rankings of the hierarchy.

First Embodiment Sales Data

As described above, FIG. 1 shows the main architectural elements of thesystem. Raw data values are stored in the data store 101. In the firstembodiment, these are data values relating to product sales. As outlinedfor the examples given above, there are several differentcharacteristics that can be associated with each individual dataelement. A particular example is given below. However, there are manydifferent types of characteristics which could also be associated andthe example should not be taken as limiting. In this particular example,the associated characteristics which are weighted are as follows:price/size of sale, time of sale, location of sale. This data isreceived and stored centrally in the data store 101. A user inputs arequest via the terminal or interface 106. The parameters of the requestcan be varied to structure the request to request data within certainboundaries or limits. In this particular example sales within a certainoverall time period are chosen, over the course of a five-year period.Certain specified locations such as branches within a specifiedgeographical location such as a city or state are also requested.

The data retrieval module 104 extracts the requested data from the datastore 101 and passes this to the processing module 105. The processingmodule 105 processes each data element and applies a set of rules. Thesecan generally be broken down into two subsets: a first subset of rulesfor presentation (colour, text etc.) as in a standard style sheet, and asecond subset of rules for functional elements (e.g. sorting). Theprocessing module 105 assigns a weighting to the characteristics of thedata. These weightings can be varied depending on the instructionscontained within the processing module 105, and can be user-assigned (auser-set or user-dictated priority approach) or assigned automatically(a conditional priority approach) and a typical example is given below.However, this should not be taken as limiting the instructions orweighting to these particular parameters. For the purposes of theexample, the weightings are manually assigned by a user (a user-set oruser-dictated priority approach), and a weighting is given on a scale of1-10. These are relative values, not absolute, so the number itself isonly important in relation to how it positions the attribute in relationto other chosen attributes.

In this example, the second rules subset consists of two subroutines:firstly sorting by function, and secondly applying a priority to eachfunction or characteristic. For each of the three characteristics of thedata elements given above (dollar value of total sales, time of sale,location of sale) a sort function is applied—e.g. sales can be sortedfrom largest dollar value to smallest. There are multiple ways to sortthe ‘time of sale’, for example by both ‘month’ and ‘year’, or by ‘salesquarter’ and ‘year’. Location of sale can also be sorted in multipleways, for example by both state and city. Next, a priority is applied toeach function or characteristic. In this example, ‘time of sale’ isweighted heavily as the user wishes this to dominate, but the user alsowishes to sort the data by both ‘sales quarter’ and ‘month’, with yeardominant over ‘sales quarter’. Therefore, time of sale—year is weightedas ‘10’, and ‘time of sale—quarter’ is rated at ‘8’. The user alsowishes to sort the sales by location, but considers this to be secondaryto time, and considers individual city locations to be secondary tostate locations. ‘Location—state’ is given a weighting of ‘7’, and‘location—city’ is given a weighting of ‘6’. ‘Dollar value of totalsales’ is given the lowest weighting of ‘3’. Chronology and location canalso be varied, but in this example the time elements are sorted in alinear chronological manner (date ascending: 2008, 2009, 2010, etc. Q1:January, February, March, Q2: April, May June, etc), and state and cityare sorted alphabetically. ‘Dollar value of total sales’ is sorted fromhighest to lowest, descending.

As ‘time of sale—year’ has been weighted the heaviest, this willdominate, so the data will be sorted by year, and then by quarter, asthis is the second most heavily weighted. The data will then be sortedby state, and then by city. Finally, the data will be sorted byprice/size of sale.

In this embodiment, the data is output as a table. The processing module105 creates a table of suitable size by calculating the maximum numbersof rows and columns required. The processing module then populates thecells. In this example, columns for the years are created first, thensub-columns showing the quarter. Rows are created to show location bystate, with sub-rows within these rows to show individual cities. Thesales are then sorted within the sub-columns and sub-rows from highestto lowest.

The first subset of rules for presentation (colour, text etc.) is alsoapplied, generating the required font, font size, colour elements forbackground, text, table etc. Shading can also be applied across cells orgroups of cells—for example blue shading through to red descendingdownwards (most to fewest) for city sales within a state. These fieldscan also be tied to the hierarchy or have their own hierarchy (defaultor otherwise). For example, certain fields can have a higher weightingfor background colour. If this field is at the top of the hierarchy,then this field will set the background colour.

This creates a grid of sales data where a user can easily find the cellsub-set which contains the sales data for a particular quarter within ayear for a particular city, and easily compare this to cell sub-sets.For example, the data for a particular state or city in a certainquarter or year can be compared to other quarters or years for the samelocation. Alternatively, the data for a particular state or city in acertain quarter or year can be compared to other locations for the sametime period.

Thus, it can be seen that for any particular combination of dataelements, a hierarchy or priority can be applied to the data elementsand characteristics thereof, that results in a style sheet beinggenerated ‘on the fly’ for given data elements. The output created bythe style sheet can be changed easily by changing the priority ofelements, which changes the order of the elements in the hierarchy. Forexample, a user could change the weighting of the data elements tochange the resulting table. If a user wishes to place greater emphasison location, the weightings for the geographical locations are changedso that they are highest in the hierarchy. ‘Location—state’ is given aweighting of ‘10’, and ‘location—city’ is given a weighting of ‘9’.‘Time of sale—year’ is weighted as ‘8’, and ‘time of sale—month’ israted at ‘7’. As before, ‘dollar value of total sales’ is given thelowest weighting, of ‘6’ or less. As before, the processing module 105creates a table of suitable size by calculating the maximum numbers ofrows and columns required. The processing module then populates thecells. In this example, columns for the states are created first, thensub-columns showing individual cities. Rows are created to show years,with sub-rows within these rows to show months. The sales are thensorted within the sub-columns and sub-rows from highest to lowest.

This change has been easily achieved by a user inputting a change in theweighting given to elements, to change their position in the hierarchy.The style sheet is generated ‘on the fly’ to create the required output.This results in less work being required than would otherwise be thecase to individually change or manually manage the functions. To achievethe same result using a normal style sheet, the style sheet would needto be manually formatted so that the output result is that which isspecifically required for a particular circumstance. For the exampleabove, the style sheet must be formatted to first sort by year, then bymonth, then by location, etc. The style sheet would then have to bereformatted to first sort by location (state then city), then by year,then by month, then by sales. That is, if a change is required, theentire style sheet is required to be re-ordered/re-formatted to outputthe changed, specific instructions.

It can be seen that less memory is required as the style sheet iscreated ‘on the fly’ as opposed to having a number of pre-formattedstyle sheets which can be applied as required to achieve the sameresult. The need for multiple or cascaded style sheets has been removed.Less processing power is required to create a hierarchy and then a stylesheet from the hierarchy than to create an end result from multiplestyle sheets. The memory required for multiple style sheets and forcascading has been eliminated. Workload is reduced (both coding andprocessing work) as an individual style sheet is not required for eachspecific situation. Also, less processing power is required as there isless to process, and the desired output is either achieved quicker, orwith less processing power, or both.

Second Embodiment Gaming

In this embodiment, data is gathered from a gaming environment such asthat shown diagrammatically in FIG. 3. In the example shown, the gamingenvironment consists of a number of gaming machines 301, and electronictables 303. Other types of gaming terminal could also be present.However, for the purposes of simplicity, this example is limited to twodifferent types of terminal: the gaming machines 301 and the electronictables 303. Each of the gaming machines 301 could be, for example, aslot machine which a single user plays at one time. Each of theelectronic tables 301 could be a multiple-user table. For example aroulette table, a blackjack table, or similar. The gaming machines andthe tables are adapted to communicate electronically with other systemsusing any suitable protocols, such as data packet protocols.

The gaming environment further includes a number of electronic cashierdevices 305 and ATMs 307 which are in communication via a Wide AreaNetwork 309 with one or more financial databases 311.

Data from all of these devices is transferred to a data store such asdata store 101. This can occur via transfer first to a localised datastore such as local store 312, with a regular data dump to the mainstore 101. Local store 312 can also compile and store related but notdirectly relevant data such as data relating to the other transactionsof customers—at restaurants, souvenir shops, bars and other relatedbusinesses 313. The location of each of the machines, tables, ATMs etcis static. The type of data measured by each of these could be bothtime- and amount-specific, and could relate to both buy-ins andpay-outs. The type of data measured by the gaming table could alsorelate to numbers of individual users using the gaming table for aparticular time period, and also to their particular location relativeto the table. For example, the stools around a blackjack table could beweight-sensitive and record when a user is occupying the location.

As for the first example, a chart could be created which shows incomerevenue against time in a table form. This would be useful for analysingbusy times—on an individual daily basis, or on a weekly, monthly oryearly basis. Peaks (such as New Year's Eve or similar) can easily beidentified. Once these peaks are identified, extra staff can be rosteredon, additional electronic cashier devices and ATMs can be activated orthese can be refilled more frequently, etc. The relative sizes of thesepeaks can also be analysed, for example by having an output in the formof a rendered heatmap or similar. This can be useful when decisions arebeing made as to numbers of extra staff or similar.

However, a user may also require an analysis of revenue againstlocation, for example for the purposes of reconfigurations to the gamingenvironment.

In this embodiment, the data store holds a ‘map’ of key features of thegaming environment. The location of the gaming machines, the table andthe ATMs are included on the map. Other key features are alsoincluded—for example, the locations of entrances and exits to the gamingenvironment, the layout of the room so that thoroughfares or similar aremapped (gaming machines tend to be aligned in facing rows to createcorridors), and the location of other key features such as bars. All ofthe raw data relating to time, frequency, amount and location istransmitted to the data store 101. As for the first example, the dataretrieval module 104 extracts the requested data from the data store 101and passes this to the processing module 105. In this embodiment, theuser forces their constraints on the inputted data elements, choosing toweight ‘location’ so that this element is at the top of the hierarchy.Revenue is ranked below location. As for the first element, theprocessing module 105 processes each element and applies the rules tocreate a style sheet which dictates the output.

The output could be, for example, a table. The location data could formthe columns of the table. This could be arranged by type of machine(e.g. all the slot machines in one column subset, followed by the gamingtables), and distance from a feature (e.g. distance from the mainentrance/exit). Revenue could be entered in sheet rows. In this manner,it can be easily seen if there is a correlation between distance from afeature and revenue, or location in relation to a feature and revenue.For example, those slot machines furthest from the entrance or exit mayhave significantly different revenues from those which are closest. Theminimum buy-in or payout can be adjusted accordingly. Positions on amulti-player gaming table can be plotted against revenue. Those at theends, or where the stools face in a certain direction may be found tohave significant differences from others (facing away from the doors orbar may distract players less). The alignment and location of the tablecan be adjusted accordingly.

By changing locational parameters, it can be seen that it is easy toreview key data relating to revenue, and thus to make decisions aboutlocational reconfiguration.

Multiple locational parameters with different weightings can also beadded to an analysis. For example the distance from the entrance/exitcould be given the highest weighting, with distance to the bar rankedbelow this (or vice-versa), and revenue ranked under these. The resultcould be displayed as a table, a heat map or any suitable outputdisplay, making it easy to analyse the results and note any that are ofinterest in terms of configuration.

A fourth (or more) element can also be added, such as time, for example.By weighting location and time above revenue in the hierarchy, hotspotsfor play can easily be identified. Gaming machines may have more usersat certain times of day, and certain machines in certain locations maybe used more frequently, by a greater number of individual users, thanothers. Analysis of location against times of the year could also beeasily carried out. Holiday evenings can be extremely busy. Locationcould be weighted to be highest in the hierarchy, with time ranked belowthis, and revenue below both. This would allow a user to easily identifyif certain games were more popular than others at certain times of theyear. For example, slot machines may be more popular than gaming tableson New Years Eve (December 31st), and game tables may be more popularthan slot machines around other holiday periods such as Chinese NewYear.

If there is a way to identify individuals within the gaming environment,this can be added as a data factor. Use of items such as cash machinescan easily be identified, as can any purchases made by credit card.Frequency and amounts of withdrawals/purchase at various locations canbe monitored. For a group of users, these transactions can be correlatedagainst time data such as time of day and frequency, and also location.The demographics and habits of those who tend towards higher spendingcan be identified, and also the factors associated with these habits.Layout and timings can be adjusted accordingly. Associated factors, suchas for example the tendency towards a consolatory drink of those whofortune does not favour can also be identified by carrying out alocation and spending analysis, and the location of the bar relative tothe game machines and tables can be adjusted accordingly.

Previously, in order to carry out these different analyses multiplecascaded style sheets were required. All of the analysis scenarios thata user might wish to run were required to be pre-thought out so that theappropriate style sheets could be coded and then ranked or cascaded asrequired in order to create an output. This not only required a greatdeal of pre-planning, and was inherently limiting as only a limitednumber of set combinations were possible, but large amounts of memorywere required to the individual style sheets, and large amounts ofprocessing power were required for running the data through the cascadedstyle sheets.

The present invention allows an appropriate style sheet to be generated‘on-the-fly’. For any particular combination of data elements, ahierarchy or priority can be applied to the data elements andcharacteristics thereof, that results in a style sheet being generated‘on the fly’ for the given data elements.

The output created by the style sheet can be very easily changed bychanging the priority of elements, which changes the order of theelements in the hierarchy. If a user wishes to place greater emphasis ondifferent locations, the weightings for the geographical locations canbe easily changed to change the output. A new style sheet is generated‘on the fly’ to create the required output. This results in much lesswork being required than would otherwise be the case to individuallychange or manually manage the functions. To achieve the same resultusing a normal style sheet, the style sheet would need to be manuallyformatted so that the output result is that which is specificallyrequired for a particular circumstance. That is, if a change isrequired, the entire style sheet is required to bere-ordered/re-formatted to output the changed, specific instructions, oralternatively the cascade order or individual ones of a number of stylesheets in a cascade are required to be changed.

It can be seen that less memory is required as the style sheet iscreated ‘on the fly’ as opposed to having a number of pre-formattedstyle sheets which can be applied as required to achieve the sameresult. The need for multiple or cascaded style sheets is completelyremoved. The result is that much less memory and much less processingpower is required to create a hierarchy and then a style sheet from thehierarchy than to create an end result from multiple style sheets. Thememory required for multiple style sheets and for cascading has beeneliminated. Workload is reduced (both coding and processing work) as anindividual style sheet is not required for each specific situation.Also, less processing power is required as there is less to process, andthe desired output is either achieved quicker, or with less processingpower, or both.

Example of Fields with Data Weightings and the Resulting Output Table

In the example below, the data elements and their style attributes arethose from the first preferred embodiment—sales data. Three attributesof the data elements have been chosen: dollar value of total sales, timeof sale, and location of sale. The data elements are sorted bycharacteristic, and a priority is applied to each attribute. A sortfunction is applied: sales are sorted from largest dollar value tosmallest. ‘Time of sale’, is sorted by two sub-attributes—year, andsales quarter.

The user wishes ‘year’ to dominate. Location of sale is sorted by twosub-attributes—both state and city, and the user wishes ‘state’ todominate. The user has set ‘time’ to dominate over ‘location’, and forboth of these to be higher in the hierarchy than ‘sales’.

This is achieved by a user manually setting priorities or weighting from1-10. These are manually applied by the user to each style attribute, asshown in the table below. These are relative values, not absolute, sothe number itself is only important in relation to how it positions theattribute in relation to other chosen attributes.

TABLE 1 relative weightings for data attributes Data element andattribute/sub-attribute Weighting ‘Time of sale - year’ 10 ‘Time ofsale - month’ 8 ‘Location - state’ 7 ‘Location - city’ 6 ‘Dollar valueof total sales’ 3

Certain other style elements are unspecified and in this example revertto a default setting e.g. the time elements are sorted in a linearchronological manner (date ascending: 2008, 2009, 2010, etc. January,February, March, etc). The default sort order for state and city in thisexample is alphabetically. However, the default could be any suitabledata representation e.g. population size. In this example, the user hasallowed ‘state’ to remain (or revert) to the default setting so that‘state’ is sorted alphabetically. As ‘dollar value of total sales’ hasbeen set by a user as a required field, this overrides the default, andonce the sales have been sorted by city within the state (as dictated bythe user weighting as shown in the table above), the city order isrearranged from highest sales to lowest, descending, as dictated by thefinal user weighting as shown in the final cell of the table above.

As ‘time of sale—year’ has been weighted the heaviest, this willdominate, so the data will first be sorted by year, and then by month asthis is the second most heavily weighted. The data will then be sortedby state, and then by city. Finally, the data will be sorted byprice/size of sale.

The data is output is a table. The processing module 105 creates a tableof suitable size by calculating the maximum numbers of rows and columnsrequired. The processing module then populates the cells. In thisexample, columns for the years are created first, then sub-columnsshowing individual sales quarters. Rows are created to show location bystate, with sub-rows within these rows to show individual cities. Thesales are then sorted within the sub-columns and sub-rows from highestto lowest. A table showing example sales data over a three-year periodfor three states, with three cities in each state, is shown below.

TABLE 2 Example Output Table Time 2010 2011 Q1 Q2 Q3 Q4 Q1 Q2 LocationArizona Phoenix 95,021 92,004 101,002 97,201 99,294 94,234 Tucson 72,72474,093 80,924 89,992 85,632 85,643 Chandler 50,295 52,887 51,456 58,00255,493 57,943 California Los Angeles 200,453 198,346 204,578 210,743211,512 212,345 San Francisco 104,582 112,576 110,268 166,457 178,357165,857 Hollister 55,678 59,345 61,285 100,456 98,587 95,284 TexasDallas 102,021 98,004 105,002 101,201 103,294 99,234 Houston 80,72478,093 84,924 93,992 89,632 89,643 Austin 54,295 56,887 55,456 62,00259,493 61,943 Time 2011 2012 Q3 Q4 Q1 Q2 Q3 Q4 Location Arizona Phoenix93,465 91,684 85,021 92,004 92,002 85,021 Tucson 80,222 71,368 71,75472,193 79,924 72,756 Chandler 49,678 46,987 50,295 52,287 51,226 50,495California Los Angeles 198,758 201,567 202,000 200,784 199,367 199,387San Francisco 104,786 108,476 107,375 110,482 109,567 111,254 Hollister53,222 52,167 63,456 60,245 57,832 54,587 Texas Dallas 97,465 91,24382,845 102,234 100,230 100,340 Houston 83,222 91,368 94,032 98,34082,954 83,456 Austin 53,678 56,987 59,765 52,345 53,002 61,456

In this example, it can be seen that overall, the style sheet presentsthe data in an easily usable format. However, it can also be seen thatthere are data points which do not fit the overall pattern generated bythe style sheet. In the example, there is data for sales quarters wherethe sales for a particular city are higher than other cities, and salesquarters where they are lower. The style sheet is arranged so that whenthis occurs, an assessment is made of the overall order, and the citywith the greatest number of higher sales quarters over the chosen dateranges is placed first or highest. See for example Q4 of 2011 and Q1 of2012. In these two instances, Houston outsells Dallas. However, in theremainder of the sales for the chosen three-year time range, Dallasconsistently outsells Houston, so Houston is shown first or aboveHouston in the rows for sales in Texas. In the output table generated bythe style sheet, the data in the four relevant table cells ishighlighted in bold. As the description indicates, a subset of rules forpresentation (colour, text etc.) is also applied, generating therequired font, font size, colour elements for background, text, tableetc. As noted, these fields can also be tied to the hierarchy or havetheir own hierarchy. In this instance, the four fields which are out ofplace in the pattern are highlighted in bold to draw the attention of auser to the data which is outside or counter to the remainder of thepattern which the data generates.

In this example, the presentation rules have also flagged (byhighlighting) another anomaly in the data: in all of the cities chosen,the sales are considerably above the average or mean for Q4 in 2010 andthe first two quarters of 2011. The presentation rules subset hasflagged these by highlighting the relevant columns. This draws theattention of the user to the relevant data. The higher sales figures inthis example are due to a nationwide marketing campaign, which hascaused a sales ‘bounce’. It can be seen that the campaign has worked forall the cities chosen. If there were data cells which remainedun-highlighted in the columns, this would also draw the eye of the user,and allow them to drill down into the data to see if there was aparticular reason that a particular area had not ‘bounced’ with the restof the nation.

While the present invention has been illustrated by the description ofthe embodiments thereof, and while the embodiments have been describedin detail, it is not the intention of the Applicant to restrict or inany way limit the scope of the appended claims to such detail. Further,the above embodiments may be implemented individually, or may becombined where compatible. Additional advantages and modifications,including combinations of the above embodiments, will readily appear tothose skilled in the art. Therefore, the invention in its broaderaspects is not limited to the specific details, representative apparatusand methods, and illustrative examples shown and described. Accordingly,departures may be made from such details without departure from thespirit or scope of the Applicant's general inventive concept.

1. In a data visualisation computing system, a computer implementedmethod of creating a style sheet for the generation of a graphicalrepresentation of a data set made up of a plurality of data elements orvalues, the method having the steps of: retrieving the data values ofthe data elements in the data set; determining the types of dataelements in the set; obtaining the style attributes associated with eachtype of data element; developing a hierarchy of style attributes; andcreating a style sheet to apply to the data set.
 2. The method asclaimed in claim 1 wherein in the steps of obtaining the styleattributes and developing a hierarchy of style attributes, the hierarchyis developed based on a pre-set or pre-existing weighting for each dataelement.
 3. The method as claimed in claim 2 wherein a user can overridethe pre-set weightings and alter the rankings of the hierarchy. 4.method as claimed in claim 1 wherein in the steps of obtaining the styleattributes and developing a hierarchy of style attributes, a user setsthe relative weightings of each of the data elements.
 5. The method asclaimed in claim 1 wherein the steps of obtaining the style attributesand developing a hierarchy of style attributes are divided into thesteps of firstly sorting the retrieved data elements by function, andsecondly applying a priority or weighting to each function.
 6. Themethod as claimed in claim 5 wherein the hierarchy is developedaccording to the weightings, the hierarchy forming the framework orbasis of a style sheet which is applied to the retrieved data elementsto format an output for the data elements.
 7. The method as claimed inclaim 1 wherein the method also includes the step of applying a set ofrules for overall presentational style, to generate the requiredelements that include but are not limited to font, font size, colourelements for background, text, table size, shading.
 8. The method asclaimed in claim 7 wherein the elements which relate to overallpresentational style are associated with the hierarchy so that theoverall appearance of the output depends on the hierarchy.
 9. The methodas claimed in claim 1 wherein in the step of retrieving the data valuesof the data elements in the data set, a user sets parameters around thedata values to be retrieved.
 10. The method as claimed in claim 9wherein the parameters include but are not limited to: a date range, atime range, a location range or location subset, a transaction range orsubset.
 11. The method as claimed in claim 1 wherein the data elementscan be any combination of, but not limited to, the following elements:time of sale, location, time of use, size of sale.
 12. The method asclaimed in claim 1 wherein a user can manually input or rearrange ahierarchy order either pre- or post-output.
 13. The method as claimed inclaim 1 wherein the data is output in any of, but not limited to, thefollowing formats: table, bar chart, pie chart, scatter plot, linechart, heat map, box spiral.
 14. The method as claimed in claim 1wherein the user set fields may be chosen from any combination of, butnot limited to, the following elements: a data range, a location rangeor number of locations, a sales range, a revenue range.
 15. The methodas claimed in claim 1 wherein the method includes the step, after thehierarchy of style attributes has been developed, of assessing theoverall pattern formed by the hierarchy and the data elements, anddeveloping the overall pattern for the output using those data elementswhich form the majority, the minority of data elements within theretrieved data which do not naturally comply overridden to fit withinthe overall pattern.
 16. The method of claim 15 wherein the method alsoincludes the step of applying a set of rules for overall presentationalstyle, the minority of data elements which do not naturally fit withinthe pattern presented in a different manner from the majority dataelements.
 17. The method as claimed in claim 15 wherein the method alsoincludes the steps of applying a set of rules for overall presentationalstyle, and assessing any results within the overall pattern that lieoutside or deviate from the mean or average by an amount, those resultspresented in a different manner from the majority data elements.
 18. Themethod as claimed in claim 17 wherein the amount is a default amount.19. The method as claimed in claim 17 wherein the amount is auser-specified amount.
 20. The method as claimed in claim 18 wherein theamount is a percentage.
 21. The method as claimed in claim 18 whereinthe amount is a numerical value based on the relevant data elements.